Stress detection during job interview using physiological signal


Mand, Ali Afzalian and Sayeed, Md. Shohel and Hossen, Md. Jakir and Zuber, Muhammad Amer Ridzuan (2022) Stress detection during job interview using physiological signal. International Journal of Electrical and Computer Engineering (IJECE), 12 (5). p. 5531. ISSN 2088-8708

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A job interview can be challenging and stressful even when one has gone through it many times. Failure to handle the stress may lead to unsuccessful delivery of their best throughout the interview session. Therefore, an alternative method which is preparing a video resume and interview before the actual interview could reduce the level of stress. An intelligent stress detection is proposed to classify individuals with different stress levels by understanding the physiological signal through electrocardiogram (ECG) signals. The Augsburg biosignal toolbox (AUBT) dataset was used to obtain the state-of-art results. Only five selected features are significant to the stress level were fed into neural network multi-layer perceptron (MLP) as the optimum classifier. This stress detection achieved an accuracy of 92.93% when tested over the video interview dataset of 10 male subjects who were recording the video resume for the analysis purposes.

Item Type: Article
Uncontrolled Keywords: Biosignal, Classification algorithm, Electrocardiogram signal, Emotion recognition, Multi-layer perceptron, Neural network
Subjects: Q Science > QA Mathematics > QA71-90 Instruments and machines > QA75.5-76.95 Electronic computers. Computer science
Divisions: Faculty of Engineering and Technology (FET)
Depositing User: Ms Nurul Iqtiani Ahmad
Date Deposited: 14 Sep 2022 01:39
Last Modified: 14 Sep 2022 01:39


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